Evaluating End-to-End Entity Linking on Domain-Specific Knowledge Bases: Learning about Ancient Technologies from Museum Collections

Sebastian Cadavid-Sanchez, Khalil Kacem, Rafael Aparecido Martins Frade, Johannes Boehm, Thomas Chaney (), Danial Lashkari and Daniel Simig ()
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Sebastian Cadavid-Sanchez: ECON - Département d'économie (Sciences Po) - Sciences Po - Sciences Po - CNRS - Centre National de la Recherche Scientifique
Khalil Kacem: ECON - Département d'économie (Sciences Po) - Sciences Po - Sciences Po - CNRS - Centre National de la Recherche Scientifique
Rafael Aparecido Martins Frade: ECON - Département d'économie (Sciences Po) - Sciences Po - Sciences Po - CNRS - Centre National de la Recherche Scientifique
Thomas Chaney: USC - University of Southern California, ECON - Département d'économie (Sciences Po) - Sciences Po - Sciences Po - CNRS - Centre National de la Recherche Scientifique
Danial Lashkari: BC - Boston College

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Abstract: To study social, economic, and historical questions, researchers in the social sciences and humanities have started to use increasingly large unstructured textual datasets. While recent advances in NLP provide many tools to efficiently process such data, most existing approaches rely on generic solutions whose performance and suitability for domain-specific tasks is not well understood. This work presents an attempt to bridge this domain gap by exploring the use of modern Entity Linking approaches for the enrichment of museum collection data. We collect a dataset comprising of more than 1700 texts annotated with 7,510 mention-entity pairs, evaluate some off-the-shelf solutions in detail using this dataset and finally fine-tune a recent end-to-end EL model on this data. We show that our fine-tuned model significantly outperforms other approaches currently available in this domain and present a proof-of-concept use case of this model. We release our dataset and our best model.

Date: 2023-05
Note: View the original document on HAL open archive server: https://sciencespo.hal.science/hal-04911147v1
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Working Paper: Evaluating End-to-End Entity Linking on Domain-Specific Knowledge Bases: Learning about Ancient Technologies from Museum Collections (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:wpaper:hal-04911147

DOI: 10.48550/arXiv.2305.14588

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